Development of a Thermo-Metallurgical Model to Predict Heating and Austenitization of 22MnB5 for Hot Forming Die Quenching

2017 ◽  
Author(s):  
M. Verma ◽  
J. R. Culham ◽  
M. Di Ciano ◽  
K. J. Daun
Keyword(s):  
Spot Welding ◽  
Present Model ◽  
Hot Forming ◽  
Austenite Formation ◽  
Hearth Furnace ◽  
First Order ◽  
Ultrahigh Strength Steel ◽  
Die Quenching ◽  
Body In White ◽  
Roller Hearth

Hot forming die quenching (HFDQ) is used to transform ultrahigh strength steel blanks into martensitic body-in-white components that are lighter than parts made from traditional mild steels, without sacrificing crash performance. The part is sometimes locally reinforced by spot-welding patches to the blanks, but the increased thickness of the patched blanks sometimes results in incomplete austenitization, which can compromise the strength of as-formed parts. This paper presents an integrated thermo-metallurgical model of the austenitization of Al-Si coated 22MnB5 within a roller hearth furnace. While previous models account for the latent heat of austenitization by heuristically adjusting the specific heat, the present model explicitly simulates austenite formation using a first-order metallurgy submodel derived from dilatometry measurements. The model is validated by comparing predicted temperatures to measurements carried out on coupons heated within a lab-scale muffle furnace and full-sized blanks heated in an industrial-scale roller hearth furnace. Finally, the model is used to optimize roller speed based on zone temperatures.

scholarly journals Minimizing the Cycle Time of a Roller Hearth Furnace for Hot-Forming Die-Quenching

2018 ◽  
Vol 418 ◽  
pp. 012016
Author(s):  
Mohit Verma ◽  
Massimo Di Ciano ◽  
J. Richard Culham ◽  
Cyrus Yau ◽  
Kyle J. Daun
Keyword(s):  
Cycle Time ◽  
Hot Forming ◽  
Hearth Furnace ◽  
Die Quenching ◽  
Roller Hearth

Development and Validation of a Thermometallurgical Model for Furnace-Based Austenitization During Hot Stamping

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
M. Verma ◽  
H. Yan ◽  
J. R. Culham ◽  
M. Di Ciano ◽  
K. J. Daun
Keyword(s):  
Hot Stamping ◽  
Austenite Phase ◽  
Phase Fraction ◽  
Manganese Steel ◽  
Model Parameters ◽  
Order Model ◽  
Hearth Furnace ◽  
Detailed Model ◽  
First Order ◽  
Roller Hearth

In hot-forming die-quenching (HFDQ) boron manganese steel blanks are heated within a roller hearth furnace, and then simultaneously quenched and formed into fully martensitic body-in-white components. Industry needs models that can predict the instantaneous temperature and austenite phase fraction within the roller furnace to diagnose problems (e.g., incomplete austenitization), forecast costs, and optimize process settings. This paper introduces a thermometallurgical model for Al–Si coated 22MnB5, consisting of a coupled heat transfer and austenitization submodels. Two candidate austenitization submodels are considered: an empirical first-order model and a model based on the detailed austenitization kinetics. In the case of the first-order model, a detailed Monte Carlo procedure is used to construct 95% credibility intervals for the blank temperature and austenite phase fraction that accounts for uncertainties in the furnace temperature and model parameters. The models are first assessed using temperature and austenite phase fractions from Al–Si coated 22MnB5 coupons heated in a laboratory-scale muffle furnace, and then used to simulate austenitization of patched blanks within an industrial roller hearth furnace. The results show that the empirical first-order model provides a more robust estimate of austenite phase fraction compared to the detailed model.

Path planning of spot welding robot based on multi-objective grey wolf algorithm

2021 ◽  
pp. 1-9
Author(s):  
Yun-Tao Zhao ◽  
Lei Gan ◽  
Wei-Gang Li ◽  
Ao Liu
Keyword(s):  
Path Planning ◽  
Density Estimation ◽  
Spot Welding ◽  
Teaching Method ◽  
Grey Wolf ◽  
Pareto Solution ◽  
Multi Objective ◽  
Operation Rules ◽  
Welding Sequence ◽  
Body In White

The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces.

scholarly journals Intercritical Annealing of 22MnB5 for Hot Forming Die Quenching

2018 ◽  
Vol 418 ◽  
pp. 012010 ◽  
Author(s):  
Haotian Yan ◽  
Massimo Di Ciano ◽  
Mohit Verma ◽  
Kyle J. Daun
Keyword(s):  
Intercritical Annealing ◽  
Hot Forming ◽  
Die Quenching

Optimizing spot welding parameters in a sheet metal assembly by neural networks and genetic algorithm

2007 ◽  
Vol 221 (7) ◽  
pp. 1175-1184 ◽  
Author(s):  
M Hamedi ◽  
M Shariatpanahi ◽  
A Mansourzadeh
Keyword(s):  
Neural Networks ◽  
Spot Welding ◽  
Welding Process ◽  
Welding Current ◽  
The Body ◽  
Welding Parameters ◽  
Automotive Body ◽  
Body In White ◽  
Assembly Operations

Deformation of the spot-welded sub-assemblies in assembly operations and the gap between the matching sub-assemblies have been quality concerns specifically in the automotive industry. Overall quality of the car body and its sub-assemblies, apart from quality of each stamped part, depends markedly on the welding process. This paper considers optimization of three important process parameters in the spot welding of the body components, namely welding current, welding time, and gun force. In this research, first the effects of these parameters on deformation of the sub-assemblies are experimentally investigated. Then neural networks and multi-objective genetic algorithms are utilized to select the optimum values of welding parameters that yield the least values of dimensional deviations in the sub-assemblies. Welding sub-assemblies with the optimized set of parameters brought all of them into the tolerance range. The proposed approach can be utilized in manufacturing sub-assemblies that can fit and match better with adjacent parts in the automotive body. It enhances quality of the joint and will result in improving overall quality of the body in white.

High Temperature Heat Transfer During Hot Forming Die Quenching of Boron Steel

2013 ◽  
Author(s):  
Etienne Caron ◽  
Kyle J. Daun ◽  
Mary A. Wells
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Cooling Rate ◽  
Hot Stamping ◽  
High Strength ◽  
Hot Forming ◽  
Boron Steel ◽  
Steel Blank ◽  
Die Quenching

Distributed mechanical properties can be obtained in ultra high strength steel parts formed via hot forming die quenching (HFDQ) by controlling the cooling rate and microstructure evolution during the quenching step. HFDQ experiments with variable cooling rates were conducted by quenching Usibor® 1500P boron steel blanks between dies pre-heated up to 600°C. The heat transfer coefficient (HTC) at the blank / die interface, which is used to determine the blank cooling rate, was evaluated via inverse heat conduction analysis. The HTC was found to increase with die temperature and stamping pressure. This heat transfer coefficient increase was attributed to macroscopic flattening of the boron steel blank as well as microscopic deformation of surface roughness peaks. At the end of the hot stamping process, the HTC reached a pressure-dependent steady-state value between 4320 and 7860 W/m2·K when the blank and die temperatures equalize.

Direct contact heating for hot forming die quenching

2016 ◽  
Vol 98 ◽  
pp. 1165-1173 ◽  
Author(s):  
J.N. Rasera ◽  
K.J. Daun ◽  
C.J. Shi ◽  
M. D'Souza
Keyword(s):  
Direct Contact ◽  
Hot Forming ◽  
Die Quenching ◽  
Contact Heating

Direct Contact Heating for Hot Forming Die Quenching

2014 ◽  
Author(s):  
J. N. Rasera ◽  
K. J. Daun ◽  
M. D’Souza
Keyword(s):  
Yield Strength ◽  
Material Characterization ◽  
Direct Contact ◽  
Hot Forming ◽  
Boron Steel ◽  
Martensitic Structure ◽  
Austenitizing Temperature ◽  
Contact Heating ◽  
Heating Technology ◽  
Roller Hearth

Most hot forming lines use slow, energy-intensive roller hearth furnaces to austenitize boron steel “blanks”. This paper describes an alternative heating technology in which blanks are austenitized by bringing them into contact with a hot monolith. The austenitizing temperature was reached in less than 30 seconds, and subsequent material characterization tests on oil-quenched blanks confirm that a fully martensitic structure is formed, and that the hardness and yield strength are comparable to furnace-treated samples. An Al-Si coating is typically used to prevent the oxidation and decarburization of the blanks within the furnace; preliminary tests found that the coating adheres to the monolith, impeding blank transfer and damaging the Al-Si-Fe ternary coating. Five interchangeable striking surfaces were assessed to see if they were less prone to adhering to the molten Al-Si coating.

scholarly journals Research on Torsional Property of Body-In-White Based on Square Box Model and Multiobjective Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yanmei Meng ◽  
Yuan Liang ◽  
Qinchuan Zhao ◽  
Johnny Qin
Keyword(s):  
Genetic Algorithm ◽  
Shear Stiffness ◽  
Box Model ◽  
Torsional Stiffness ◽  
Design Stage ◽  
Multiobjective Genetic Algorithm ◽  
First Order ◽  
Body In White ◽  
Torsional Frequency ◽  
Square Box

In order to assess the performance of a vehicle in the conceptual design stage, a square box model was proposed to predict the torsional stiffness and the first-order torsional frequency of Body-in-White. The structure of Body-in-White was decomposed into eight simple structural surfaces, from which a square box model was constructed. Based on the finite element method, modified shear stiffness of each simple structure surface was calculated and the torsional stiffness was obtained. Then, simple structural surfaces of Body-in-White were constructed into an eight degree-of-freedom series spring system to calculate the first-order torsional frequency. Furthermore, a multiobjective genetic algorithm was used to determine the thickness and structural reinforcement of panels with small stiffness, so as to achieve the goal of increasing the stiffness while reducing the mass of the panel. The result shows that the optimal values of thickness are reduced by around 9.9 percent without affecting their performance by the proposed method. Compared to the prediction results obtained with the complicated numerical simulation, the relative error of the square box model in predicting the torsional stiffness is 6.04 percent and in predicting the first-order torsional frequency is 0.95 percent, indicating that the prediction model is effective.

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